Nonlocal Self-Similarity (NSS) prioritization plays an important role in image restoration, but it is worthy of further research that how to make full use of this prior to improve the performance of image restoration. An image denoising via weighted nuclear norm minimization and Gaussian Mixed Model (GMM) was proposed. Firstly, the clean NSS image blocks of the natural image were trained by GMM, and then the trained GMM was used to guide the degraded image to produce NSS image blocks. Then, the weighted nuclear norm minimization was used to realize image denoising, an extended model was proposed by modifying the fidelity item, and the corresponding convergent algorithm was given. The simulation results show, compared with some advanced algorithms such as Block Matching with 3D filtering (BM3D), Learned Simultaneous Sparse Coding (LSSC) and Weighted Nuclear Norm Minimization (WNNM), the proposed method improves the Peak Signal-to-Noise Ratio (PSNR) by 0.11 to 0.49 dB.